Azure Machine Learning Deployment at Scale Using ARM and AMLPS.

Introduction

In this post, I will demonstrate how to do a simple but useful scenario when managing Azure Machine Learning Workspaces and Experiments, copying all the experiments under one workspace, deploy a new workspace using ARM (Azure Resource Manager) in another region, and then copy the experiment under the newly deployed workspace.

At this point, you should see all your workspaces listed.
We are now ready to configure AMLPS, for this you will need to retrieve the workspace ID, and the authorization token (detailed step by step for AMLPS configuration). We will update config.json (in c:\amlps) for simplicity. From the above list of workspaces, select the one you want to duplicate. In my example, the workspace name is “workspaceus”.

Deploy

Now that we have installed and configured all the tools, we can start our example. We will copy a workspace and its experiment located in “South Central US” and copy it to “West Europe”. Below is an illustration representing the different steps in our journey.

Get workspace information

We will export all experiment graph as a JSON file so we can import them back on the new workspace.